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Microglia play an important role in the maintenance and neuroprotection of the central nervous system (CNS) by removing pathogens, damaged neurons, and plaques. Recent observations emphasize that the promotion and development of neurodegenerative diseases (NDs) are closely related to microglial activation. In this review, we summarize the contribution of microglial activation and its associated mechanisms in NDs, such as epilepsy, Alzheimer's disease (AD), Parkinson's disease (PD), and Huntington's disease (HD), based on recent observations. This review also briefly introduces experimental animal models of epilepsy, AD, PD, and HD. Thus, this review provides a better understanding of microglial functions in the development of NDs, suggesting that microglial targeting could be an effective therapeutic strategy for these diseases.
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http://dx.doi.org/10.3390/biomedicines9101449 | DOI Listing |
Nanoscale Horiz
September 2025
CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Chinese Academy of Sciences and National Center for Nanoscience and Technology of China, Beijing 100190, China.
Central nervous system (CNS) diseases, including neurodegenerative diseases, stroke, brain tumors, and others, result in poor quality of life and can cause substantial disability. Not all CNS diseases are amenable to surgical approaches, so drug development is important for disease treatment. Unfortunately, there are few drugs currently available for CNS diseases.
View Article and Find Full Text PDFMol Biol Cell
September 2025
Department of Neurobiology & Anatomy, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030 USA.
Autophagy is critical for the homeostasis and function of neurons, as misregulation of autophagy has been implicated in age-related neurodegenerative diseases and neuron-specific knockdown of early autophagy genes results in early neurodegeneration in mice. We previously found that autophagosome formation decreases with age in murine neurons. Sex differences have been intensely studied in neurodegenerative diseases, but whether sex differences influence autophagy at the neuronal level have not been investigated.
View Article and Find Full Text PDFInt J Plant Anim Environ Sci
August 2025
Department of Translational Research, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, Pomona, CA 91766, USA.
Neurological disorders, such as Alzheimer's disease, Parkinson's disease, epilepsy, spinal cord injuries, and traumatic brain injuries, represent substantial global health challenges due to their chronic and often progressive nature. While allopathic medicine offers a range of pharmacological interventions aimed at managing symptoms and mitigating disease progression, it is accompanied by limitations, including adverse side effects, the development of drug resistance, and incomplete efficacy. In parallel, phytochemicals-bioactive compounds derived from plants-are receiving increased attention for their potential neuroprotective, antioxidant, and anti-inflammatory properties.
View Article and Find Full Text PDFResearch (Wash D C)
September 2025
NHC Key Laboratory of Tropical Disease Control, School of Life Sciences and Medical Technology, Hainan Medical University, Haikou, Hainan 571199, China.
Aging is characterized by a gradual decline in the functionality of all the organs and tissues, leading to various diseases. As the global population ages, the urgency to develop effective anti-aging strategies becomes increasingly critical due to the growing severity of associated health problems. Immunotherapy offers novel and promising approaches to combat aging by utilizing approaches including vaccines, antibodies, and cytokines to target specific aging-related molecules and pathways.
View Article and Find Full Text PDFFront Comput Neurosci
August 2025
Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States.
Artificial neural networks are limited in the number of patterns that they can store and accurately recall, with capacity constraints arising from factors such as network size, architectural structure, pattern sparsity, and pattern dissimilarity. Exceeding these limits leads to recall errors, eventually leading to catastrophic forgetting, which is a major challenge in continual learning. In this study, we characterize the theoretical maximum memory capacity of single-layer feedforward networks as a function of these parameters.
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